Over the last twenty years, electronic trading systems have become commonplace in the financial markets for trading a wide variety of instruments such as equities, foreign exchange (FX) products, commodities and derivatives as well as fixed income products and many other financial instruments.
Many types of electronic trading systems exist, using different trading models. Examples include RFQ (Request for Quote) based systems, anonymous matching systems and auction based systems. An example of anonymous matching system is disclosed in U.S. Pat. No. 6,996,541 Togher et al, the content of which is incorporated herein by reference. Togher describes a distributed matching system in which traders connected to the system through a communications network submit orders into the system to buy or sell financial instruments. Maker orders are displayed to other traders on the system who can respond to those orders with their own orders which will be matched with the visible maker orders in accordance with matching rules to execute a trade. Typically, the system will receive maker orders from all parties and construct an order book based, for example, an order price and time of order receipt. For ease of interpretation only the best order or best few orders will be displayed to other traders on their screens and counterparty traders respond to the best orders they see.
Many trading systems are based on a centralised host computer which matches incoming maker and taker orders, maintains order books and administers credit limits. The host computer may also be responsible for distributing market related data, generating deal tickets after a trade has been executed and maintaining records of activity on the system. Some trading systems such as that described in Togher et al mentioned above, operate as a distributed model in which the matching engine is split into a number of separate matching engines. This approach is attractive in a global trading system where latency issues can affect the fairness of access to a centralised system from different parts of the world. The Togher distributed system, as implemented by ICAP Plc in its EBS trading platform, has a number of matching engines each located geographically in a main financial market. As these markets operate at different times of the day, many of the trades will be between parties who are operating in the same geographical region and the matching may be performed locally at the regional matching engine. Other trades may involve two separate matching engines in two separate geographical regions. An example would be a trade conducted in the afternoon in London between a London based trader and a New York based trader where it is the morning and the markets are open.
Latency issues are present in any system which connects parties over large distances. The distributed architecture goes some way to addressing latency issues. Fairness issues are of concern and distributed systems provide improved fairness compared to centralised systems. However, the rise of algorithmic trading has highlighted latency issues. Algorithmic trading, also known as High Frequency Trading (HFT), replaces human traders with electronic platforms which enter orders automatically in accordance with a trading algorithm. The orders are generated in response to received market data such as the price or size of orders in the market. In a distributed system such as the EBS system, market views are sent to each trading entity, such as a bank's trading floor, periodically giving that trading entity an update of the market book. These market views are distributed in turn to each floor giving the first trading floors that receive market views a slight advantage over floors that receive the views later, and in particular over the last trading floor to receive the market update. Latency issues caused by the relative proximity of the trading floors to the computers distributing the market views can exacerbate this advantage. This problem is dealt with, to an extent, by the distribution method and apparatus disclosed in U.S. Pat. No. 8,446, 801 (Howorka et al) the contents of which are incorporated by reference. Howorka introduces a random component into the order in which market updates are distributed so that the time at which a given trading floor receives market data relative to other trading floors gradually changes over time. This approach goes some way to evening out unfairness over time.
Thus, known electronic trading systems have utilised some measures to address latency issues and to address unfairness in access to the system. However, they are unable to deal with discrepancies in the speed at which parties trading on the system can enter orders into the system. This is an issue which is largely out of the control of the trading system operator. In view of the speed at which many financial markets operate, there is a strong motivation for trading entities such as hedge funds and banks to invest heavily in hardware, software and communications technology that will ensure their orders reach the trading system as quickly as possible. This approach requires heavy financial investment on behalf of the trading entities and introduces a strong element of unfairness in that it gives an advantage to the larger entities which are more able to make the investment required. The problem can be less severe on systems that operate on a private communications network but worse on systems that use a public network such as the Internet for communications between the trading entities and the trading system.
U.S. Pat. No. 7,461,026 assigned to Trading Technologies, Inc attempts to address this problem. Market data is sent from a host system to client devices through synchronised local communication services so that data can be displayed simultaneously or near simultaneously at each client device. Transaction data sent from the client devices to the host system is also received via the local communication servers and the ordering of that transaction data is based, at least in part, on when the local communication servers received the transaction data from the client devices. The transaction data may include order information and the transaction messages may be prioritised by determining a travel time from a first network device to the host exchange and then determines a similar travel time for a second device. When a transaction message is sent from a first client device the receipt time is determined. Similarly, the receipt time of messages from the second device is measured and the host system can then use the known travel times for the two devices to prioritise the first and second transaction messages at the host exchange.
While this approach goes someway to addressing the issue, it is complex and relies on a fore-knowledge of travel times and a constancy of travel time for repeated transactions from the same device. The approach may not be able to cope well with orders submitted from mobile devices such as tablets or phones which are beginning to be used in the markets as travel time will vary depending on the location of the device.
There is, therefore, a need for an improved approach to the problem of fairness in order entry into electronic trading systems.